Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a clustering of images into a plurality of semantic categories. In one aspect, a method comprises: training a categorization neural network, comprising, at each of a plurality of iterations: processing an image depicting an object using the categorization neural network to generate (i) a current prediction for whether the image depicts an object or a background region, and (ii) a current embedding of the image; determining a plurality of current cluster centers based on the current values of the categorization neural network parameters, wherein each cluster center represents a respective semantic category; and determining a gradient of an objective function that includes a classification loss and a clustering loss, wherein the clustering loss depends on a similarity between the current embedding of the image and the current cluster centers.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a clustering of images into a plurality of semantic categories. In one aspect, a method comprises: training a categorization neural network, comprising, at each of a plurality of iterations: processing an image depicting an object using the categorization neural network to generate (i) a current prediction for whether the image depicts an object or a background region, and (ii) a current embedding of the image; determining a plurality of current cluster centers based on the current values of the categorization neural network parameters, wherein each cluster center represents a respective semantic category; and determining a gradient of an objective function that includes a classification loss and a clustering loss, wherein the clustering loss depends on a similarity between the current embedding of the image and the current cluster centers.

Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.